Today, we’re excited to launch TimescaleDB hyperfunctions, a series of SQL functions within TimescaleDB that make it easier to manipulate and analyz

Introducing hyperfunctions: new SQL functions to simplify working with time-series data in PostgreSQL

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2021-07-13 17:30:07

Today, we’re excited to launch TimescaleDB hyperfunctions, a series of SQL functions within TimescaleDB that make it easier to manipulate and analyze time-series data in PostgreSQL with fewer lines of code. You can use hyperfunctions to calculate percentile approximations of data, compute time-weighted averages, downsample and smooth data, and perform faster COUNT DISTINCT queries using approximations. Moreover, hyperfunctions are “easy” to use: you call a hyperfunction using the same SQL syntax you know and love.

At Timescale, our mission is to enable every software developer to store, analyze, and build on top of their time-series data, so that they can measure what matters in their world: IoT devices, IT systems, marketing analytics, user behavior, financial metrics, and more. (For example, we’ve built a free multi-node, petabyte-scale, time-series database; a multi-cloud, fully-managed service for time-series data; and Promscale, an open-source analytics platform for Prometheus monitoring data.)

We made the decision early in the design of TimescaleDB to build on top of PostgreSQL. We believed then, as we do now, that building on the world’s fastest-growing database would have numerous benefits for our customers. Perhaps the biggest of these advantages is in developer productivity. Developers can use the tools and frameworks they know and love and bring all their skills and expertise with SQL with them.

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